This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.2716
Accuracy: 1.0
F1 Macro: 1.0
Precision Macro: 1.0
Recall Macro: 1.0
Precision Dry: 1.0
Recall Dry: 1.0
F1 Dry: 1.0
Precision Humid: 1.0
Recall Humid: 1.0
F1 Humid: 1.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Macro	Precision Macro	Recall Macro	Precision Dry	Recall Dry	F1 Dry	Precision Humid	Recall Humid	F1 Humid
No log	1.0	1	0.7190	0.5	0.4505	0.5686	0.5354	0.4706	0.8889	0.6154	0.6667	0.1818	0.2857
No log	2.0	2	0.5751	0.75	0.7494	0.7708	0.7626	0.6667	0.8889	0.7619	0.875	0.6364	0.7368
No log	3.0	3	0.4168	0.95	0.9488	0.9583	0.9444	1.0	0.8889	0.9412	0.9167	1.0	0.9565
No log	4.0	4	0.2716	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
No log	5.0	5	0.1546	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
No log	6.0	6	0.0931	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
No log	7.0	7	0.0530	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
No log	8.0	8	0.0353	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
No log	9.0	9	0.0176	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.334	10.0	10	0.0109	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.334	11.0	11	0.0071	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.334	12.0	12	0.0050	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.334	13.0	13	0.0037	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.334	14.0	14	0.0028	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.334	15.0	15	0.0022	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.334	16.0	16	0.0017	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.334	17.0	17	0.0014	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.334	18.0	18	0.0011	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.334	19.0	19	0.0009	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0041	20.0	20	0.0008	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0041	21.0	21	0.0007	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0041	22.0	22	0.0006	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0041	23.0	23	0.0005	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0041	24.0	24	0.0005	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0041	25.0	25	0.0005	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0041	26.0	26	0.0004	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0041	27.0	27	0.0004	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0041	28.0	28	0.0004	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0041	29.0	29	0.0004	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	30.0	30	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	31.0	31	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	32.0	32	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	33.0	33	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	34.0	34	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	35.0	35	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	36.0	36	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	37.0	37	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	38.0	38	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0004	39.0	39	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	40.0	40	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	41.0	41	0.0003	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	42.0	42	0.0002	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	43.0	43	0.0002	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	44.0	44	0.0002	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	45.0	45	0.0002	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	46.0	46	0.0002	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	47.0	47	0.0002	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	48.0	48	0.0002	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	49.0	49	0.0002	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
0.0002	50.0	50	0.0002	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0

Framework versions

Transformers 4.56.1
Pytorch 2.5.1+cu124
Datasets 4.0.0
Tokenizers 0.22.0

dacunaq/vit-base-patch16-224-finetuned-humid-binary-2

vit-base-patch16-224-finetuned-humid-binary-2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dacunaq/
vit-base-patch16-224-finetuned-humid-binary-2